The Evolution of the Data World and Challenges in Spreading Information

Please Rate and Review us on your podcast app of choice!

Get involved with Data Mesh Understanding's free community roundtables and introductions: https://landing.datameshunderstanding.com/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

Episode list and links to all available episode transcripts here.

Provided as a free resource by Data Mesh Understanding. Get in touch with Scott on LinkedIn.

Transcript for this episode (link) provided by Starburst. You can download their Data Products for Dummies e-book (info-gated) here and their Data Mesh for Dummies e-book (info gated) here.

Jen's LinkedIn: https://www.linkedin.com/in/jentedrow/

Martina's LinkedIn: https://www.linkedin.com/in/martina-ivanicova/

Xavier's LinkedIn: https://www.linkedin.com/in/xgumara/

Xavier's blog post on data as a product versus data products: https://towardsdatascience.com/data-as-a-product-vs-data-products-what-are-the-differences-b43ddbb0f123

Results of Jen's survey 'The State of Data as a Product in the Real World' (NOT info-gated 😎👍): https://pathfinderproduct.com/wp-content/uploads/2023/12/2023-State-of-DaaP-Real-World-Study.pdf?mtm_campaign=daap-study&mtm_source=pp-blog&mtm_content=pdf-daap-study

In this episode, guest host Jen Tedrow, Jen Tedrow, Director, Product Management at Pathfinder Product, a Test Double Operation (guest of episode #98) facilitated a discussion with Martina Ivaničová, Data Engineering Manager and Tech Ambassador at Kiwi.com (guest of episode #112), and Xavier Gumara Rigol, Data Engineering Manager at Oda (guest of episode #40). As per usual, all guests were only reflecting their own views.

The topic for this panel was data as a product generally and especially how can we actually apply it to data in the real world. This is Scott's #1 most important aspect to get when it comes to doing data - especially data mesh - well. It's the holistic practice of applying product management approaches to data. It ends up shaping all the other data mesh principles and is a much broader topic than data mesh is in his view. But it can also be quite simple in concept when you really boil it down, it just takes patience and focus.

Scott note: I wanted to share my takeaways rather than trying to reflect the nuance of the panelists' views individually.

Scott's Top Takeaways:

1. At its core, data as a product is more an organizational mindset approach than anything else. It is something you work towards. It's not an overnight change but getting the mindset right first - at least with a small core group - will help the organization figure out how to best move towards treating your data as a product.
2. Data as a product and data product thinking must not fall into only thinking about data products - It's product thinking about data! In general product management, it isn't about creating a product, it's about creating an experience for the customer that generates value for them in some way. The best way to do that sustainably in data is a data product. But the value and experience are the point, that's your focus, the data product is merely a vehicle to deliver those.
3. Think about good and bad product experiences. The incomprehensible manuals and unintuitive design of a bad product. The awesome tutorials and documentation around a good one. We need to create easy paths for people to not only discover our data products but discover the best ways to generate value from them.
4. Data as a product includes considering your data sourcing strategy. If you think about a physical good, you don't just have the labor to put it together, you need to consider the materials that need to be combined to create the product. It's not just about what parts you have laying around, you manage the supply chain - hopefully reliably and scalably - to make your widgets. The same should be part of data product practices. It's not just what data you have but what data you will need.
5. If we actually want our organizations to be data-driven (whatever that means to you 😅), people need to be able to rely on the data. If we want to embed leveraging data into the DNA, the day-to-day operations, of the company, we need to make our data creation and management processes reliable. The best way to do that is via data products because it makes it easy and reliable for consumers to leverage data. The data isn't the point, it's the mechanism to drive business value.
6. A first step on the road to your organization treating data as a product is getting away from the data team as a service mindset. That they are ticket takers. Data shouldn't be a cost center or ticket-taking organization rather than a value generating one.
7. A key aspect of good products is usability. We need to focus more on usability in data. That is somewhat wrapped into user experience but there are other aspects that are even more often overlooked than UX. A lot of that usability falls on the platform as well so there isn't a different user experience for each data product.
8. Moving to a data as a product approach, instilling that data as a product mindset in your organization, will be hard. And it is quite a bit of cognitive load for people who haven't focused on data historically. Look to introduce the concept and changes needed to implement data as a product over time. This isn't a switch you flip.

Other Important Takeaways (many touch on similar points from different aspects):

1. Data as a product is a lot about the mindset and approach you bring to data. What have we learned from product management - in software and elsewhere - that we can bring to data?
2. Most good general product experiences - at least on the consumer side - don't need a ton of hand-holding. Can we actually get there on data? Do we want it to be that easy since it's still easy to misinterpret the data? This balance will be different for every organization and probably every data product.
3. Relatedly, fully self-service is a real question. You want to lower the amount of information requests to data owners but some of those questions can be value generating. So you want to build out the experience - especially documentation - to answer the basics but there's a question of how far you go relative to trying to document everything.
4. There's a major question about how much change management is involved in learning to treat your data as a product. Is it just a mindset shift? Probably not. But then how do you actually change the organization to start focusing on data as a product?
5. Jen said, "… the primary purpose of data as a product is to maximize data as utility."
6. There isn't a single solution or approach to 'solving' product management. There won't for data as a product either 😅 prepare yourself and your teams for that. It's going to take sustained learning and evolution to get better and better. There is no 'done' but there is a ton of value to accrue along that learning journey.
7. It's okay - if not ideal - to have multiple things in your organization called 'data products'. Not all have to meet the data mesh definition. But make it clear to people what you mean around data products - potentially call them mesh data products - or their thinking on data products will be, "okay, but just what the heck is a data product?" 😅
8. Software product management is to software products as data product management (the application of data as a product) is to data products. No one thinks software products and product management are the same. We shouldn't in data either.
9. Data work and learning how to do data well both generally have a high cognitive load. Be prepared for that. Don't expect everyone to get it right away, whether that is the why of treating data as a product or the how do we actually do this :)
10. Data as a product will be hard to instill even inside your data team. Again, this will take time and you have to let people know the why and the how. Similarly, you need to be prepared for sustained effort to communicate the benefits to those in the business. People aren't jumping up and down to own their own data and especially not to do it well 😅
11. Are people ready for the best product approaches in data? Doing product management well is about trying, fast failing, learning, then iterating. Are people ready for there to be more continual change in how data is served?
12. Part of product management is stakeholder management and especially communication. In data, we need to move past requirement gathering but we also have to find better ways to communicate value and get - and then retain - buy-in.
13. Product management, at the end of the day, is about capturing value through creating value for others. With data as a product, you need to understand what value is expected to be created from your work and where you might increase that value. But data doesn't have inherent value unless it is used so you need to stimulate data usage to create value.
14. An important benefit of managing your data as a product is the improvements in data user experience. That means more time spent on creating value instead of wrangling data.
15. Part of good product management is product marketing. That means discovering what data should exist but also finding champions, internally marketing successes, etc. You need people to see the value of the data work to get them to want to lean in. People aren't paying close enough attention to inherently know the impact of the data work, you have to tell them 😅
16. Products have owners. Treating your data as a product means your data has strong ownership. It's easy to say you are creating data products but really instilling that ownership is crucial to doing data at scale reliably.

Learn more about Data Mesh Understanding: https://datameshunderstanding.com/about

Data Mesh Radio is hosted by Scott Hirleman. If you want to connect with Scott, reach out to him on LinkedIn: https://www.linkedin.com/in/scotthirleman/

If you want to learn more and/or join the Data Mesh Learning Community, see here: https://datameshlearning.com/community/

If you want to be a guest or give feedback (suggestions for topics, comments, etc.), please see here

All music used this episode was found on PixaBay and was created by (including slight edits by Scott Hirleman): Lesfm, MondayHopes, SergeQuadrado, ItsWatR, Lexin_Music, and/or nevesf